I spent a fair amount of time at a prior job looking at document management systems and enterprise search. As part of the knowledge management effort, it was key to figure out how people found the information or documents they needed. In reviewing user behavior, I have identified four different types of searches for documents:
- Fetch
- Recall
- Research
- Precedent
Below is more information on each type of search and user expectations for search results. I will also discuss how well a document management system (DMS) or enterprise search tool will handle the different types of searches.
Fetch
A “fetch” search is when the user has a document ID (with a Document Management System) or a file name (with a file server system).
The user would expect the single document to be returned. There should be no need for relevancy rankings.
The “fetch” search is the most basic of the four types of searches. It is such a basic part of a DMS and works so well in a DMS that most users do not even think of it as a search. Nonetheless, it is the most common search and the most important. A user expects to be able to get a specific document back instantly for editing or reuse, without having to interpret search results.
A “fetch” is core functionality of a DMS. An enterprise search tool would fall short in this type of search. The DMS is keyed to find specific metadata from the document profile. The enterprise search tool typically uses the metadata to influence the relevancy rankings of a particular document.
Recall
A “recall” search is when the user knows the document exists and has some specific information about the document that the user can distinguish it from other documents.
Examples are: documents edited in the last five days, all the documents for a particular matter, all of the purchase agreement for a client.
The user will expect a a list of documents that will be over-inclusive, but the list will have information to distinguish the particular document the user is looking for from the rest of the documents.
A DMS excels at this type of search and is core functionality for a DMS. The enterprise search will generally not perform well at this type of search. For the DMS search to be successful, user input is required to make sure the metadata/profile of the document is accurate.
Research
A “research” search is when the user is looking for documents on a topic. The user may not know if any documents on the topic even exist. The search is typically for keywords in the document.
An example is: information on “arms-dealing”.
A user will expect a list of documents displayed by relevancy.
An enterprise search tool excels at this type of search. The user is looking for terms in the document. The enterprise search tool can use its algorithm to identify which documents have the most treatment of the search terms.
A typical DMS will fall short on a “research” search. A typical DMS does not rank searches based on relevancy. If a search yielded dozens or more results, the user would have no reference as to where to start a review of search results. A typical DMS also has an inferior text search engine.
It is the frustration when running a “research” search that users cry out for an enterprise search tool.
Precedent
A “precedent” search is a search for a model document.
Generally, the key to finding a good precedent is knowing the context in which a document was previously used, rather than text in the document itself.
An example is: ” a purchase and sale agreement for a retail shopping center in Florida”. “Purchase and Sale Agreement” will be in the text of the document and the name of the document. But “Florida” and “retail shopping center” may not appear in the text of the document. If they do appear, they would appear infrequently.
They key to making a precedent search working is leveraging the document metadata against other systems. For instance, we require users to assign a document to a particular matter. We plan to use that matter identification to pull information from other sources and impute that information on the document.
The other key to a precedent search is using a faceted search to narrow the search results using the additional metadata.
A version of this post appeared in my old blog KM Space: 4 Types of Search.
